Fluorescent molecular sensors have become valuable tools in the analytical biosciences owing to their sensitive detection mode, down to the level of a single molecule, the feasibility of naked eye visualization, their versatility, and their small size, which enable them to penetrate the cell membrane and track the rise and fall of various bio-analytes within living cells. Although fluorescent sensors that utilize photo-induced electron transfer (PET), electronic energy transfer (EET) (or fluorescence resonance energy transfer (FRET)), and internal charge transfer (ICT) processes have been developed and used to detect various proteins, most of them suffer from a high background signal that complicates their use in complex biochemical mixtures and within cells.
Extensive efforts have been taken to develop non-reductionist approaches to disease diagnosis. Profiling the expression of multiple proteins, rather than detecting individual protein analytes, has been explored as a means of improving diagnostic accuracy and better understanding the parameters affecting disease states. Potential techniques for proteome-wide analysis include two-dimensional gel electrophoresis, mass spectrometry, and antibody microarrays.
A promising method for obtaining multiplexed protein analysis involves the use of antibodies, which can bind and detect the proteins of interest with high affinity and selectivity. This approach has found widespread applications in medical diagnosis, however, the need for producing an antibody for each target and for using stepwise protocols, in addition to their high cost, relative instability and the difficulties associated with quantifications hamper high-throughput analysis.
Cross-reactive sensor arrays, inspired by the mammalian olfactory system, have recently emerged as an alternative detection method that may address these limitations. When the “nose/tongue” approach is used, proteins can be rapidly differentiated using an array of non-selective synthetic receptors that, in combination, generate a unique optical “fingerprint” upon interacting with each protein. Unlike antibody arrays, which operate according to the “lock-and key” paradigms, arrays that rely on “differential sensing” do not require manufacturing multiple antibodies or using technically challenging procedures. As a result, such systems can straightforwardly discriminate among multiple different proteins as well as profile protein mixtures in blood or urine, which may indicate of disease states.
Despite the numerous advantages of pattern-generating arrays, applications for this technology in medical diagnostics are largely limited by the difficulty of non-selective receptors to operate within biological mixtures. Human serum contains more than 20,000 proteins, of which only ˜20 proteins constitute about 99% of the serum protein mass. Thus, although such systems can effectively discriminate among combinations and concentrations of abundant serum proteins, in their current form, they cannot be applied for detecting most disease biomarkers.
Matrix Metalloproteases (MMPs) family of enzymes comprising more than 20 zinc-dependent endopeptidases that share a similar, zinc-dependent binding site, and are capable of degrading virtually every component of the extracellular matrix (ECM). These isozymes can be divided into several subgroups, based on their structures or preferential substrates, which include, among others, collagen, gelatin, and various extracellular matrix proteins.
Owing to their role in tumor growth, metastasis, and angiogenesis, MMPs are considered as important therapeutic targets for treating human cancers. In addition, high levels of members of the MMP family in serum, urine, or tissue have been identified in a variety of human cancers, including breast, pancreatic, bladder, colorectal, ovarian, and prostate cancer (MMP-1 is identified in breast cancer, lung cancer and colorectal cancer; MMP-2 is identified in pancreas cancer, bladder cancer, colorectal cancer, ovarian cancer, prostate cancer and brain cancer; MMP-7 is identified in pancreas cancer, lung cancer and colorectal cancer; MMP-9 is identified in breast cancer, pancreas cancer, bladder cancer, lung cancer, colorectal cancer, ovarian cancer, prostate cancer and brain cancer). Thus, MMPs are considered to be promising biomarkers for different cancers, both for diagnostic and prognostic purposes.
Human Glutathione S-Transferases (GSTs) are a family of widely distributed enzymes that play a role in cell detoxification by catalyzing the conjugation of γ-L-glutamyl-L-cysteinylglycine (gluthation) to a broad range of electrophilic endo toxines and xenobiotics that are subsequently excreted from the cell. This activity is a crucial part of a self-defense mechanism that protects the organism from toxic and sometimes carcinogenic species.
Human soluble GSTs can be mainly subdivided into 7 classes, namely, α (A), μ (M), π (P), θ (T), σ (S), κ (K), and ω (O), which share a similar GST binding domain but differ in their surface characteristics as well as in their tissue distribution (FIG. 1D). Comparative analysis of GST expression in normal and diseased tissues or serum has shown a clear correlation between their expression profiles and disease states. For example, abnormal tissue expression of GST α (A) has been associated with an increased risk for colorectal cancer, ovarian cancer, and clear cell renal cell carcinoma. μ (M) class expression alteration was detected in cases of lung, colon, and bladder cancer, whereas the π (P) class isozymes are overexpressed in the majority of human tumors.
Moreover, several metabolic conditions led to excretion of GST proteins into urine or the blood circulation. For instance, the presence of GST-A in urine or in blood plasma is an early biomarker for hepatocellular damage, whereas elevated serum levels of GST-P is an indicator of various cancers (breast, lung and gastric cancers). GST-A1 is an indicator for colorectal, prostate, breast and lung cancers. GST-A2 is an indicator for prostate and lung cancers. GST-M1 is an indicator for prostate and breast cancers. An issue of high importance is distinguishing between combinations of several GST subtypes in urine. For example, measurements of GST-A and GST-P in urine provide information about the site of renal tubular injury. In addition, a combination of plasma α and π levels was proposed as a tool to predict and monitor graft failure or regeneration following living donor liver transplantation.
Fibroblast Growth Factors (FGFs) are a family comprising 22 heparin-binding proteins whose over-expression is associated with different types of cancers. Pattern-based detection arrays, specific for this class of proteins, could therefore facilitate their differentiation and, more importantly, might be able to distinguish between medicinally relevant samples involving different combinations or concentrations of FGFs.
Estrogen Receptors (ERα) have been mainly implicated in the development and progression of breast cancer, where much research has focused on identifying alterations within the coding sequence of these receptors in clinical samples. Mutations within ERα have been identified in several different diseases, indicating that the most common technique for determining tumor ER status, namely, immunohistochemical assays or ligand binding assays, might not be efficient for identifying ERs with abnormal ligand binding capacity or reduced functionality. Therefore, pattern-based detection arrays, specific for ERs, might serve as an additional tool for characterizing ER biomarkers.
There is a need for an integrated sensing method, which utilizes both the “lock and key” and “differential sensing” strategies for discriminating among low concentration protein biomarkers in biological mixtures. With this approach cross-reactive sensor arrays can be “tuned” to generate patterns that reflect the composition of specific protein groups, even in the presence of highly abundant serum proteins or within human urine.
The above examples not only stress the importance of developing methods for high-throughput protein analysis in biological fluids but also highlight GSTs, MMPs FGFs and ERs as potential biomarkers for detecting early stages of various diseases, including cancer.